import argparse
import datetime
import json
import os
import random
import time
from pathlib import Path

import numpy as np
import ruamel.yaml as yaml
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist

import utils
from data import create_dataset, create_sampler, create_loader
from models.blip_pretrain import blip_pretrain
from utils import warmup_lr_schedule, step_lr_schedule


def train(model, data_loader, optimizer, epoch, device, config):
    # train
    model.train()

    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))
    metric_logger.add_meter('loss_ita', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
    metric_logger.add_meter('loss_itm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
    metric_logger.add_meter('loss_lm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))

    header = 'Train Epoch: [{}]'.format(epoch)
    print_freq = 50

    if config['laion_path']:
        data_loader.dataset.reload_laion(epoch)

    data_loader.sampler.set_epoch(epoch)

    for i, (image, caption) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):

        if epoch == 0:
            warmup_lr_schedule(optimizer, i, config['warmup_steps'], config['warmup_lr'], config['init_lr'])

        optimizer.zero_grad()

        image = image.to(device, non_blocking=True)

        # ramp up alpha in the first 2 epochs
        alpha = config['alpha'] * min(1, (epoch * len(data_loader) + i) / (2 * len(data_loader)))

        loss_ita, loss_itm, loss_lm = model(image, caption, alpha=alpha)
        loss = loss_ita + loss_itm + loss_lm

        loss.backward()
        optimizer.step()

        metric_logger.update(loss_ita=loss_ita.item())
        metric_logger.update(loss_itm=loss_itm.item())
        metric_logger.update(loss_lm=loss_lm.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])

        # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger.global_avg())
    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}


def main(args, config):
    utils.init_distributed_mode(args)

    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    cudnn.benchmark = True

    #### Dataset #### 
    print("Creating dataset")
    datasets = [create_dataset('pretrain', config, min_scale=0.2)]
    print('number of training samples: %d' % len(datasets[0]))

    num_tasks = utils.get_world_size()
    global_rank = utils.get_rank()
    samplers = create_sampler(datasets, [True], num_tasks, global_rank)

    data_loader = \
    create_loader(datasets, samplers, batch_size=[config['batch_size']], num_workers=[4], is_trains=[True],
                  collate_fns=[None])[0]

    #### Model #### 
    print("Creating model")
    model = blip_pretrain(image_size=config['image_size'], vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'],
                          vit_ckpt_layer=config['vit_ckpt_layer'], queue_size=config['queue_size'])

    model = model.to(device)

    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])

    start_epoch = 0
    if args.checkpoint:
        checkpoint = torch.load(args.checkpoint, map_location='cpu')
        state_dict = checkpoint['model']
        model.load_state_dict(state_dict)

        optimizer.load_state_dict(checkpoint['optimizer'])
        start_epoch = checkpoint['epoch'] + 1
        print('resume checkpoint from %s' % args.checkpoint)

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module

    print("Start training")
    start_time = time.time()
    for epoch in range(start_epoch, config['max_epoch']):

        step_lr_schedule(optimizer, epoch, config['init_lr'], config['min_lr'], config['lr_decay_rate'])

        train_stats = train(model, data_loader, optimizer, epoch, device, config)
        if utils.is_main_process():
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         'epoch': epoch,
                         }
            save_obj = {
                'model': model_without_ddp.state_dict(),
                'optimizer': optimizer.state_dict(),
                'config': config,
                'epoch': epoch,
            }
            torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth' % epoch))

            with open(os.path.join(args.output_dir, "log.txt"), "a") as f:
                f.write(json.dumps(log_stats) + "\n")

        dist.barrier()

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', default='./configs/pretrain.yaml')
    parser.add_argument('--output_dir', default='output/Pretrain')
    parser.add_argument('--checkpoint', default='')
    parser.add_argument('--evaluate', action='store_true')
    parser.add_argument('--device', default='cuda')
    parser.add_argument('--seed', default=42, type=int)
    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    parser.add_argument('--distributed', default=True, type=bool)
    args = parser.parse_args()

    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)

    Path(args.output_dir).mkdir(parents=True, exist_ok=True)

    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))

    main(args, config)
